This paper presents a new technique for automatically generating test data. The technique is based on mutation analysis and creates test data that approximates relative adequacy. The technique is a fault-based technique that uses algebraic constraints to describe test cases designed to find particular types of faults. A set of tools, collectively called Godzilla, has been implemented that automatically generates constraints and solves them to create test cases for unit and module testing. Godzilla has been integrated with the Mothra testing system and has been used as an effective way to generate test data that kill program mutants. The paper includes an initial list of constraints and discusses some of the problems that have been solved to develop the complete implementation of the technique.

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We show how model checking and symbolic execution can be used to generate test inputs to achieve structural coverage of code that manipulates complex data structures. We focus on obtaining branch-coverage during unit testing of some of the core methods of the red-black tree implementation in the Java TreeMap library, using the Java PathFinder model checker. Three di#erent test generation techniques will be introduced and compared, namely, straight model checking of the code, model checking used in a black-box fashion to generate all inputs up to a fixed size, and lastly, model checking used during white-box test input generation. The main contribution of this work is to show how e#cient white-box test input generation can be done for code manipulating complex data, taking into account complex method preconditions.

This paper presents a technique that uses a genetic algorithm for automatic test-data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition-use pair in the program under test. The test-data-generation technique was implemented in a tool called TGen in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random test-data generator, called Random, was also implemented. Both TGen and Random were used to experiment with the generation of test-data for statement and branch coverage of six programs.

This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function [Miller and Spooner, 1976, Korel, 1990]. In our work, the function is minimized by using one of two genetic algorithms in place of the local minimization techniques used in earlier research. We describe the implementation of our GA-based system, and examine the effectiveness of this approach on a number of programs, one of which is significantly larger than those for which results have previously been reported in the literature. We also examine the effect of program complexity on the test data generation problem by executing our system on a number of synthetic programs that have varying complexities. 1 Introduction An important aspect of software testing involves judging how well a series of test inputs tests a piece of code. Usuall...

This article presents a testing methodology that adapts data flow adequacy criteria and coverage monitoring to the task of testing spreadsheets. To accommodate the evaluation model used with spreadsheets, and the interactive process by which they are created, our methodology is incremental. To accommodate the users of spreadsheet languages, we provide an interface to our methodology that does not require an understanding of testing theory. We have implemented our testing methodology in the context of the Forms/3 visual spreadsheet language. We report on the methodology, its time and space costs, and the mapping from the testing strategy to the user interface. In an empirical study, we found that test suites created according to our methodology detected, on average, 81% of the faults in a set of faulty spreadsheets, significantly outperforming randomly generated test suites

One of the major costs in a software project is the construction of test-data. This paper outlines a generalised test-case data generation framework based on optimisation techniques. The framework can incorporate a number of testing criteria, for both functional and non-functional properties. Application of the optimisation framework to testing specification failures and exception conditions is illustrated. The results of a number of small case studies are presented and show the efficiency and effectiveness of this dynamic optimisation-base approach to generating test-data. 1.1 Keywords Automatic test-case generation, software testing, formal specifications, exception conditions, optimisation techniques, simulated annealing. 2

An important problem that arises in path oriented testing is the generation of test data that causes a program to follow a given path. In this paper, we present a novel program execution based approach using an iterative relaxation method to address the above problem. In this method, test data generation is initiated with an arbitrarily chosen input from a given domain. This input is then iteratively refined to obtain an input on which all the branch predicates on the given path evaluate to the desired outcome. In each iteration the program statements relevant to the evaluation of each branch predicate on the path are executed, and a set of linear constraints is derived. The constraints are then solved to obtain the increments for the input. These increments are added to the current input to obtain the input for the next iteration. The relaxation technique used in deriving the constraints provides feedback on the amount by which each input variable should be adjusted for the branches o...

We have extended the software model checker BLAST to automatically generate test suites that guarantee full coverage with respect to a given predicate. More precisely, given a C program and a target predicate p, BLAST determines the set L of program locations which program execution can reach with p true, and automatically generates a set of test vectors that exhibit the truth of p at all locations in L. We have used BLAST to generate test suites and to detect dead code in C programs with up to 30 K lines of code. The analysis and test-vector generation is fully automatic (no user intervention) and exact (no false positives).

Structural testing criteria are mandated in many software development standards and guidelines. The process of generating test-data to achieve 100 % coverage of a given structural coverage metric is labour intensive and expensive. This paper presents an approach to automate the generation of such test-data. The test-data generation is based on the application of a dynamic optimisation-based search for the required test-data. The same approach can be be generalised to solve other test-data generation problems. Three such applications are discussed { boundary value analysis, assertion/run-time exception testing and component re-use testing. Aprototype tool-set has been developed to facilitate the automatic generation of test-data for these structural testing problems. The results of preliminary experiments using this technique and the prototype tool-set are presented and show the e ciency and e ectiveness of this approach. 1